Interoperability in healthcare cannot be achieved without mapping local data to standardized terminology. In this paper, we investigate the performance of different approaches for implementing HL7 FHIR Terminology Module operations using a benchmarking methodology, to gather evidence on the benefits and pitfalls of these methods in terms of performance from the point-of-view of a terminology client. The approaches perform very differently, while having a local client-side cache for all operations is of supreme importance. The results of our investigation show that careful consideration of the integration environment, potential bottlenecks, and implementation strategies is required.
@inproceedings{wiedekopf_benchmarking_2023,title={Performance Benchmarking of FHIR Terminology Operations in ETL Jobs},author={Wiedekopf, Joshua and Drenkhahn, Cora and Ingenerf, Josef},booktitle={Medical Informatics Europe 2023},year={2023},month=may}
2023
MIMIC-IV on FHIR: converting a decade of in-patient data into an exchangeable, interoperable format
Bennett, Alex M, Ulrich, Hannes, Damme, Philip,
Wiedekopf, Joshua, and Johnson, Alistair E W
Journal of the American Medical Informatics Association Jan 2023
Convert the Medical Information Mart for Intensive Care (MIMIC)-IV database into Health Level 7 Fast Healthcare Interoperability Resources (FHIR). Additionally, generate and publish an openly available demo of the resources, and create a FHIR Implementation Guide to support and clarify the usage of MIMIC-IV on FHIR.FHIR profiles and terminology system of MIMIC-IV were modeled from the base FHIR R4 resources. Data and terminology were reorganized from the relational structure into FHIR according to the profiles. Resources generated were validated for conformance with the FHIR profiles. Finally, FHIR resources were published as newline delimited JSON files and the profiles were packaged into an implementation guide.The modeling of MIMIC-IV in FHIR resulted in 25 profiles, 2 extensions, 35 ValueSets, and 34 CodeSystems. An implementation guide encompassing the FHIR modeling can be accessed at mimic.mit.edu/fhir/mimic. The generated demo dataset contained 100 patients and over 915 000 resources. The full dataset contained 315 000 patients covering approximately 5 840 000 resources. The final datasets in NDJSON format are accessible on PhysioNet.Our work highlights the challenges and benefits of generating a real-world FHIR store. The challenges arise from terminology mapping and profiling modeling decisions. The benefits come from the extensively validated openly accessible data created as a result of the modeling work.The newly created MIMIC-IV on FHIR provides one of the first accessible deidentified critical care FHIR datasets. The extensive real-world data found in MIMIC-IV on FHIR will be invaluable for research and the development of healthcare applications.
@article{bennett_mimicfhir_2023,doi={10.1093/jamia/ocad002},selected={true},url={https://doi.org/10.1093/jamia/ocad002},year={2023},month=jan,publisher={Oxford University Press ({OUP})},author={Bennett, Alex M and Ulrich, Hannes and van Damme, Philip and Wiedekopf, Joshua and Johnson, Alistair E W},title={{MIMIC}-{IV} on {FHIR}: converting a decade of in-patient data into an exchangeable, interoperable format},journal={Journal of the American Medical Informatics Association}}
2022
TermiCron – Bridging the Gap Between FHIR Terminology Servers and Metadata Repositories
The large variability of data models, specifications, and interpretations of data elements is particular to the healthcare domain. Achieving semantic interoperability is the first step to enable reuse of healthcare data. To ensure interoperability, metadata repositories (MDR) are increasingly used to manage data elements on a structural level, while terminology servers (TS) manage the ontologies, terminologies, coding systems and value sets on a semantic level. In practice, however, this strict separation is not always followed; instead, semantical information is stored and maintained directly in the MDR, as a link between both systems is missing. This may be reasonable up to a certain level of complexity, but it quickly reaches its limitations with increasing complexity. The goal of this approach is to combine both components in a compatible manner. We present TermiCron, a synchronization engine that provides synchronized value sets from TS in MDRs, including versioning and annotations. Prototypical results were shown for the terminology server Ontoserver and two established MDR systems. Bridging the semantic and structural gap between the two infrastructure components, this approach enables shared use of metadata and reuse of corresponding health information by establishing a clear separation of the two systems and thus serves to strengthen reuse as well as to increase quality.
@incollection{wiedekopf_termicron_2022,author={Wiedekopf, Joshua and Ulrich, Hannes and Drenkhahn, Cora and Kock-Schoppenhauer, Ann-Kristin and Ingenerf, Josef},booktitle={{MEDINFO} 2021: One World, One Health {\textendash} Global Partnership for Digital Innovation},publisher={{IOS} Press},title={{TermiCron} {\textendash} Bridging the Gap Between {FHIR} Terminology Servers and Metadata Repositories},year={2022},month=jun,doi={10.3233/shti220034}}
TerminoDiff – Detecting Semantic Differences in HL7 FHIR CodeSystems
While HL7 FHIR and its terminology package have seen a rapid uptake by the research community, in no small part due to the wide availability of tooling and resources, there are some areas where tool availability is still lacking. In particular, the comparison of terminological resources, which supports the work of terminologists and implementers alike, has not yet been sufficiently addressed. Hence, we present TerminoDiff, an application to semantically compare FHIR R4 CodeSystem resources. Our tool considers differences across all levels required, i.e. metadata and concept differences, as well as differences in the edge graph, and surfaces them in a visually digestible fashion.
@incollection{wiedekopf_terminodiff_2022,selected={true},author={Wiedekopf, Joshua and Drenkhahn, Cora and Rosenau, Lorenz and Ulrich, Hannes and Kock-Schoppenhauer, Ann-Kristin and Ingenerf, Josef},booktitle={Studies in Health Technology and Informatics},publisher={{IOS} Press},title={{TerminoDiff} {\textendash} Detecting Semantic Differences in {HL}7 {FHIR} {CodeSystems}},year={2022},month=may,doi={10.3233/shti220475}}
Data integration and exchange are becoming more crucial with the increasing amount of distributed systems and ever-growing amounts of data. This need is also widely known in medical research and not yet comprehensively solved. Practical implementation steps will demonstrate the different challenges in the context of the National Medical Informatics Initiative in Germany. Top-down versus bottom-up approaches as general methods of standard-based data integration in healthcare will be discussed and illustrated in the process of building up Medical Data Integration Centers. As practical examples, the use cases Infection Control, Cardiology, and Molecular Tumor Board, will be presented. Finally, limitations that prevent the use of theoretically recommended data integration methods in the particular field of medical informatics are illustrated.
@article{kockschopp_medical_2021,selected={true},location={Cham},title={Medical Data Engineering – Theory and Practice},volume={1481},isbn={978-3-030-87656-2 978-3-030-87657-9},url={https://link.springer.com/10.1007/978-3-030-87657-9_21},pages={269--284},journal={Advances in Model and Data Engineering in the Digitalization Era},publisher={Springer International Publishing},author={Kock-Schoppenhauer, Ann-Kristin and Schreiweis, Björn and Ulrich, Hannes and Reimer, Niklas and Wiedekopf, Joshua and Kinast, Benjamin and Busch, Hauke and Bergh, Björn and Ingenerf, Josef},editor={Bellatreche, Ladjel and Chernishev, George and Corral, Antonio and Ouchani, Samir and Vain, Jüri},year={2021},month=oct,langid={english},doi={10.1007/978-3-030-87657-9_21},note={Series Title: Communications in Computer and Information Science}}
Providing ART-DECOR ValueSets via FHIR Terminology Servers – A Technical Report
To ensure semantic interoperability within healthcare systems, using common, curated terminological systems to identify relevant concepts is of fundamental importance. The HL7 FHIR standard specifies means of modelling terminological systems and appropriate ways of accessing and querying these artefacts within a terminology server. Hence, initiatives towards healthcare interoperability like IHE specify not only software interfaces, but also common codes in the form of value sets and code systems. The way in which these coding tables are provided is not necessarily compatible to the current version of the HL7 FHIR specification and therefore cannot be used with current HL7 FHIR-based terminology servers. This work demonstrates a conversion of terminological resources specified by the Integrating the Healthcare Initiative in the ART-DECOR platform, partly available in HL7 FHIR, to ensure that they can be used within a HL7 FHIR-based terminological server. The approach itself can be used for other terminological resources specified within ART-DECOR but can also be used as the basis for other code-driven conversions of proprietary coding schemes.
@article{wiedekopf_artdecor_2021,title={Providing {ART}-{DECOR} {ValueSets} via {FHIR} Terminology Servers – A Technical Report},isbn={978-1-64368-206-8 978-1-64368-207-5},url={https://ebooks.iospress.nl/doi/10.3233/SHTI210550},journal={Studies in Health Technology and Informatics},publisher={{IOS} Press},author={Wiedekopf, Joshua and Drenkhahn, Cora and Ulrich, Hannes and Kock-Schoppenhauer, Ann-Kristin and Ingenerf, Josef},editor={Röhrig, Rainer and Beißbarth, Tim and König, Jochem and Ose, Claudia and Rauch, Geraldine and Sax, Ulrich and Schreiweis, Björn and Sedlmayr, Martin},year={2021},month=sep,doi={10.3233/SHTI210550}}
Hands on the Medical Informatics Initiative Core Data Set - Lessons Learned from Converting the MIMIC-IV
With the steady increase in the connectivity of the healthcare system, new requirements and challenges are emerging. In addition to the seamless exchange of data between service providers on a national level, the local legacy data must also meet the new requirements. For this purpose, the applications used must be tested securely and sufficiently. However, the availability of suitable and realistic test data is not always given. Therefore, this study deals with the creation of test data based on real electronic health record data provided by the Medical Information Mart for Intensive Care (MIMIC-IV) database. In addition to converting the data to the current FHIR R4, conversion to the core data sets of the German Medical Informatics Initiative was also presented and made available. The test data was generated to simulate a legacy data transfer. Moreover, four different FHIR servers were tested for performance. This study is the first step toward comparable test scenarios around shared datasets and promotes comparability among providers on a national level.
@article{ulrich_hands_2021,title={Hands on the Medical Informatics Initiative Core Data Set - Lessons Learned from Converting the {MIMIC}-{IV}},volume={283},issn={1879-8365},doi={10.3233/SHTI210549},pages={119--126},journal={Studies in Health Technology and Informatics},shortjournal={Stud Health Technol Inform},author={Ulrich, Hannes and Behrend, Paul and Wiedekopf, Joshua and Drenkhahn, Cora and Kock-Schoppenhauer, Ann-Kristin and Ingenerf, Josef},year={2021},month=sep,pmid={34545827},keywords={Data Integration, Delivery of Health Care, Electronic Health Records, {HL}7 {FHIR}, Information Dissemination, Medical Informatics, {MIMIC}-{IV}}}
Desiderata for a Synthetic Clinical Data Generator
The current movement in Medical Informatics towards comprehensive Electronic Health Records (EHRs) has enabled a wide range of secondary use cases for this data. However, due to a number of well-justified concerns and barriers, especially with regards to information privacy, access to real medical records by researchers is often not possible, and indeed not always required. An appealing alternative to the use of real patient data is the employment of a generator for realistic, yet synthetic, EHRs. However, we have identified a number of shortcomings in prior works, especially with regards to the adaptability of the projects to the requirements of the German healthcare system. Based on three case studies, we define a non-exhaustive list of requirements for an ideal generator project that can be used in a wide range of localities and settings, to address and enable future work in this regard.
@article{wiedekopf_desiderata_2021,title={Desiderata for a Synthetic Clinical Data Generator},volume={281},issn={1879-8365},doi={10.3233/SHTI210122},pages={68--72},journal={Studies in Health Technology and Informatics},shortjournal={Stud Health Technol Inform},author={Wiedekopf, Joshua and Ulrich, Hannes and Essenwanger, Andrea and Kiel, Alexander and Kock-Schoppenhauer, Ann-Kristin and Ingenerf, Josef},year={2021},month=may,pmid={34042707},keywords={Humans, Electronic Health Records, Medical Informatics, Privacy, Realistic Synthetic Electronic Health Records, Requirements, {RS}-{EHR}, Secondary Use, Synthetic Data}}
2020
Using FHIR terminology services-based tools for mapping of local microbiological pathogen terms to SNOMED CT at a German university hospital
@inproceedings{drenkhahn_using_2020,title={Using {FHIR} terminology services-based tools for mapping of local microbiological pathogen terms to {SNOMED} {CT} at a German university hospital},booktitle={16. {DVMD}-Fachtagung},author={Drenkhahn, Cora and Burmester, Sebastian and Ballout, Sarah and Ulrich, Hannes and Wiedekopf, Joshua and Ingenerf, Josef},year={2020},month=oct}